forked from mindspore-Ecosystem/mindspore
101 lines
4.0 KiB
Python
101 lines
4.0 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Inference Interface"""
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import sys
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import argparse
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from mindspore.train.model import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.nn import Loss, Top1CategoricalAccuracy, Top5CategoricalAccuracy
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from mindspore import context
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from mindspore import nn
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from src.dataset import create_dataset_cifar10
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from src.utils import count_params
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from src.hournasnet import hournasnet
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from easydict import EasyDict as edict
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parser = argparse.ArgumentParser(description='Evaluation')
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parser.add_argument('--data_path', type=str, default='/home/workspace/mindspore_dataset/',
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metavar='DIR', help='path to dataset')
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parser.add_argument('--model', default='hournas_f_c10', type=str, metavar='MODEL',
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help='Name of model to train (default: "tinynet_c"')
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parser.add_argument('--num-classes', type=int, default=10, metavar='N',
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help='number of label classes (default: 10)')
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parser.add_argument('-b', '--batch-size', type=int, default=256, metavar='N',
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help='input batch size for training (default: 256)')
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parser.add_argument('-j', '--workers', type=int, default=4, metavar='N',
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help='how many training processes to use (default: 4)')
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parser.add_argument('--ckpt', type=str, default='./ms_hournas_f_c10.ckpt',
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help='model checkpoint to load')
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parser.add_argument('--GPU', action='store_true', default=True,
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help='Use GPU for training (default: True)')
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parser.add_argument('--dataset_sink', action='store_true', default=True)
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parser.add_argument('--image-size', type=int, default=32, metavar='N',
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help='input image size (default: 32)')
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def main():
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"""Main entrance for training"""
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args = parser.parse_args()
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print(sys.argv)
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#context.set_context(mode=context.GRAPH_MODE)
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context.set_context(mode=context.PYNATIVE_MODE)
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if args.GPU:
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context.set_context(device_target='GPU')
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# parse model argument
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assert args.model.startswith(
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"hournas"), "Only Tinynet models are supported."
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#_, sub_name = args.model.split("_")
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net = hournasnet(args.model,
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num_classes=args.num_classes,
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drop_rate=0.0,
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drop_connect_rate=0.0,
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global_pool="avg",
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bn_tf=False,
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bn_momentum=None,
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bn_eps=None)
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print(net)
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print("Total number of parameters:", count_params(net))
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cfg = edict({'image_height': args.image_size, 'image_width': args.image_size,})
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cfg.batch_size = args.batch_size
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print(cfg)
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#input_size = net.default_cfg['input_size'][1]
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val_data_url = args.data_path #os.path.join(args.data_path, 'val')
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val_dataset = create_dataset_cifar10(val_data_url, repeat_num=1, training=False, cifar_cfg=cfg)
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loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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eval_metrics = {'Validation-Loss': Loss(),
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'Top1-Acc': Top1CategoricalAccuracy(),
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'Top5-Acc': Top5CategoricalAccuracy()}
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ckpt = load_checkpoint(args.ckpt)
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load_param_into_net(net, ckpt)
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net.set_train(False)
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model = Model(net, loss, metrics=eval_metrics)
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metrics = model.eval(val_dataset, dataset_sink_mode=False)
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print(metrics)
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if __name__ == '__main__':
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main()
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